KernelGen - GPU Technology Conference

More documents

Recommendations

Info

OpenACC: no pointer arithmetics.Compiler cannot parallelize loops containing pointer arithmetics:sincos.cvoid sincos(int nx, int ny, int nz, float* x, float* y, float* xy) {float *xp = x, *yp = y, *xyp = xy;#pragma acc parallelfor (int k = 0; k < nz; k++)for (int j = 0; j < ny; j++)for (int i = 0; i < nx; i++). *(xyp++) = sin(*(xp++)) + cos(*(yp++));}$ makepgcc -fast -Minfo=accel -ta=nvidia,time -Mcuda=keepgpu,keepbin,keepptx,ptxinfo -c ../sincos.c -o sincos.oPGC-W-0155-.Pointer assignments are not supported in accelerator regions: xyp (../sincos.c: 34)PGC-W-0155-Accelerator region ignored (../sincos.c: 29)PGC/x86-64 Linux 13.2-0: compilation completed with warnings20 / 75
<strong>KernelGen</strong>: pointer arithmetics support.In LLVM/Polly used by <strong>KernelGen</strong> all arrays accesses are lowered to pointers, thus there is nodifference between indexed arrays and pointer arithmetics, both are supported:sincos.cvoid sincos(int nx, int ny, int nz, float* x, float* y, float* xy) {float *xp = x, *yp = y, *xyp = xy;#pragma acc parallelfor (int k = 0; k < nz; k++)for (int j = 0; j < ny; j++)for (int i = 0; i < nx; i++). *(xyp++) = sin(*(xp++)) + cos(*(yp++));}Launching kernel __kernelgen_sincos_loop_10blockDim = { 32, 4, 4 }gridDim = { 16, 128, 16 }Finishing kernel __kernelgen_sincos_loop_10__kernelgen_sincos_loop_10 time = 2.298868e-02 sec21 / 75
Page 4 and 5: Simple example: Fortran.Compile the
Page 6 and 7: Simple example: C.Compile the Fortr
Page 8 and 9: OpenACC: no external calls.OpenACC
Page 10 and 11: KernelGen: and external static libr
Page 12 and 13: KernelGen: smart pointers tracking.
Page 14 and 15: KernelGen: smart pointers tracking.
Page 16 and 17: Other nice features..Comfortable ac
Page 18 and 19: Parallelizing Fortran array-wise co
Page 24 and 25: absolute speedup against PGI versio
Page 26 and 27: Performance factors: compute grid.2
Page 28 and 29: Performance factors: code optimizat
Page 30 and 31: Performance factors: code generatio
Page 32 and 33: absolute speedup against CPU versio
Page 34 and 35: Parallelism detection.KernelGen ana
Page 36 and 37: Cooperation with MPI..KernelGen nat
Page 38 and 39: KernelGen compiler pipeline.KernelG
Page 40 and 41: GPU-directed execution.GPUidleseria
Page 42 and 43: KernelGen execution workflow.Scenar
Page 62 and 63: Code generation brief walk-through.
Page 70 and 71:
Conclusions.1 KernelGen project imp
Page 72 and 73:
Testing COSMO and WRF.KernelGen is
Page 74 and 75:
Collaboration proposal.KernelGen te
show all

KernelGen - GPU Technology Conference

Create successful ePaper yourself

Delete template?

Save as template?